Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/682
Title: | On-line adaptive chaotic demodulator based on radial-basis-function neural networks | Authors: | Feng, J Tse, CKM |
Issue Date: | 2001 | Source: | Physical review. E, Statistical, nonlinear, and soft matter physics, v. 63, no. 2 II, 2001, 026202, p. 1-10 | Abstract: | Chaotic modulation is a useful technique for spread spectrum communication. In this paper, an on-line adaptive chaotic demodulator based on a radial-basis-function (RBF) neural network is proposed and designed. The demodulator is implemented by an on-line adaptive learning algorithm, which takes advantage of the good approximation capability of the RBF network and the tracking ability of the extended Kalman filter. It is demonstrated that, provided the modulating parameter varies slowly, spread spectrum signals contaminated by additive white Gaussian noise in a channel can be tracked in a time window, and the modulating parameter, which carries useful messages, can be estimated using the least-square fit. The Henon map is chosen as the chaos generator. Four test message signals, namely, square-wave, sine-wave, speech and image signals, are used to evaluate the performance. The results verify the ability of the demodulator in tracking the dynamics of the chaotic carrier as well as retrieving the message signal from a noisy channel. | Keywords: | Chaotic demodulator Henon map Spread spectrum communication Adaptive learning algorithm |
Publisher: | American Physical Society | Journal: | Physical review. E, Statistical, nonlinear, and soft matter physics | ISSN: | 1539-3755 | EISSN: | 1550-2376 | DOI: | 10.1103/PhysRevE.63.026202 | Rights: | Copyright 2001 by the American Physical Society. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
chaotic-demodulator_01.pdf | 270.01 kB | Adobe PDF | View/Open |
Page views
136
Last Week
1
1
Last month
Citations as of Apr 14, 2024
Downloads
204
Citations as of Apr 14, 2024
SCOPUSTM
Citations
30
Last Week
0
0
Last month
0
0
Citations as of Apr 19, 2024
WEB OF SCIENCETM
Citations
25
Last Week
0
0
Last month
0
0
Citations as of Apr 18, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.